tests/testthat/test-rbootnoise.R
In aloy/lmeresampler: Bootstrap Methods for Nested Linear Mixed-Effects Models
#The purpose of the updated rbootnoise tests is to perform rough comparisons tolerating relatively large deviations from the earlier-acquired reference data. The tests try to catch possible large deviations caused by significant technical issues. Without the highly controlled containers (not applicable in the context of cross-platform R CMD Checks) the exact technical reproducibility cannot be established. For example, it is known that even the different versions of R can cause varying behaviour of the fundamental set.seed() on the same underlying system, an unavoidable technical curiosity accepted by the community. https://stackoverflow.com/questions/47199415/is-set-seed-consistent-over-different-versions-of-r-and-ubuntu
library(lme4, quietly = TRUE)
data(jsp728, package = "lmeresampler")
# ==============================================================================
context("rbootnoise")
# ==============================================================================
test_that("Compare rbootnoise = 0 to lmeresampler 0.2.2 results before the implementation of the feature",{
#Reference data creator: Ilmari Tamminen
load("./reference_data_for_rbootnoise_test_A071022.RData")
#Obtained with the following specs:
#> sessionInfo()
#R version 4.2.1 (2022-06-23 ucrt)
#Platform: x86_64-w64-mingw32/x64 (64-bit)
#Running under: Windows 10 x64 (build 19044)
#Matrix products: default
#locale:
#[1] LC_COLLATE=Finnish_Finland.utf8 LC_CTYPE=Finnish_Finland.utf8 LC_MONETARY=Finnish_Finland.utf8
#[4] LC_NUMERIC=C LC_TIME=Finnish_Finland.utf8
#attached base packages:
#[1] stats graphics grDevices utils datasets methods base
#other attached packages:
#[1] lmeresampler_0.2.2 lme4_1.1-29 Matrix_1.4-1
#loaded via a namespace (and not attached):
#[1] Rcpp_1.0.8.3 lubridate_1.8.0 lattice_0.20-45 tidyr_1.2.1 prettyunits_1.1.1
#[6] ps_1.7.1 rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2 R6_2.5.1
#[11] plyr_1.8.7 HLMdiag_0.5.0 ggplot2_3.3.6 pillar_1.8.1 rlang_1.0.6
#[16] curl_4.3.2 rstudioapi_0.13 minqa_1.2.4 callr_3.7.0 nloptr_2.0.3
#[21] desc_1.4.1 diagonals_6.4.0 devtools_2.4.3 splines_4.2.1 statmod_1.4.37
#[26] stringr_1.4.1 munsell_0.5.0 compiler_4.2.1 janitor_2.1.0 pkgconfig_2.0.3
#[31] pkgbuild_1.3.1 mgcv_1.8-40 tidyselect_1.2.0 tibble_3.1.7 fansi_1.0.3
#[36] crayon_1.5.1 dplyr_1.0.10 withr_2.5.0 MASS_7.3-57 distributional_0.3.1
#[41] ggdist_3.2.0 grid_4.2.1 nlme_3.1-158 gtable_0.3.1 lifecycle_1.0.3
#[46] magrittr_2.0.3 scales_1.2.1 cli_3.3.0 stringi_1.7.8 cachem_1.0.6
#[51] farver_2.1.1 reshape2_1.4.4 fs_1.5.2 remotes_2.4.2 snakecase_0.11.0
#[56] ellipsis_0.3.2 generics_0.1.3 vctrs_0.4.1 boot_1.3-28 tools_4.2.1
#[61] forcats_0.5.2 rcmdcheck_1.4.0 glue_1.6.2 purrr_0.3.4 processx_3.6.1
#[66] pkgload_1.3.0 fastmap_1.1.0 nlmeU_0.70-9 colorspace_2.0-3 xopen_1.0.0
#[71] sessioninfo_1.2.2 memoise_2.0.1 usethis_2.1.6
#RStudio 2022.02.0+443 "Prairie Trillium" Release (9f7969398b90468440a501cf065295d9050bb776, 2022-02-16) for Windows
#Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) QtWebEngine/5.12.8 Chrome/69.0.3497.128 Safari/537.36
#Obtained with the following code:
#library(lme4)
#library(lmeresampler)
#model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
#nsim <- 100
#set.seed(123)
#A071022ref <- bootstrap(model, .f = fixef, type = "residual", B = nsim)
model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
nsim <- 100
set.seed(123)
boo <- bootstrap(model, .f = fixef, type = "residual", B = nsim)
maxreldev <- (A071022ref[["stats"]][,2:5] - boo[["stats"]][,2:5])/A071022ref[["stats"]][,2:5]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
maxreldev <- (A071022ref[["replicates"]] - boo[["replicates"]])/A071022ref[["replicates"]]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
})
test_that("Compare rbootnoise = 0.0001 to the results of the first implementation of the feature",{
#Reference data creator: Ilmari Tamminen
load("./reference_data_for_rbootnoise_test_B071022.RData")
#Obtained with the following specs:
#> sessionInfo()
#R version 4.2.1 (2022-06-23 ucrt)
#Platform: x86_64-w64-mingw32/x64 (64-bit)
#Running under: Windows 10 x64 (build 19044)
#Matrix products: default
#locale:
#[1] LC_COLLATE=Finnish_Finland.utf8 LC_CTYPE=Finnish_Finland.utf8 LC_MONETARY=Finnish_Finland.utf8
#[4] LC_NUMERIC=C LC_TIME=Finnish_Finland.utf8
#attached base packages:
#[1] stats graphics grDevices utils datasets methods base
#other attached packages:
#[1] lmeresampler_0.2.1.99999 lme4_1.1-29 Matrix_1.4-1
#loaded via a namespace (and not attached):
#[1] Rcpp_1.0.8.3 lubridate_1.8.0 lattice_0.20-45 tidyr_1.2.1 prettyunits_1.1.1
#[6] ps_1.7.1 rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2 R6_2.5.1
#[11] plyr_1.8.7 HLMdiag_0.5.0 ggplot2_3.3.6 pillar_1.8.1 rlang_1.0.6
#[16] curl_4.3.2 rstudioapi_0.13 minqa_1.2.4 callr_3.7.0 nloptr_2.0.3
#[21] desc_1.4.1 devtools_2.4.3 diagonals_6.4.0 splines_4.2.1 statmod_1.4.37
#[26] stringr_1.4.1 munsell_0.5.0 compiler_4.2.1 janitor_2.1.0 pkgconfig_2.0.3
#[31] pkgbuild_1.3.1 mgcv_1.8-40 tidyselect_1.1.2 tibble_3.1.7 fansi_1.0.3
#[36] crayon_1.5.1 dplyr_1.0.10 withr_2.5.0 MASS_7.3-57 distributional_0.3.1
#[41] ggdist_3.2.0 grid_4.2.1 nlme_3.1-158 gtable_0.3.1 lifecycle_1.0.3
#[46] magrittr_2.0.3 scales_1.2.1 cli_3.3.0 stringi_1.7.8 cachem_1.0.6
#[51] farver_2.1.1 reshape2_1.4.4 fs_1.5.2 remotes_2.4.2 snakecase_0.11.0
#[56] ellipsis_0.3.2 generics_0.1.3 vctrs_0.4.1 boot_1.3-28 tools_4.2.1
#[61] forcats_0.5.2 rcmdcheck_1.4.0 glue_1.6.2 purrr_0.3.4 processx_3.6.1
#[66] pkgload_1.3.0 fastmap_1.1.0 nlmeU_0.70-9 colorspace_2.0-3 xopen_1.0.0
#[71] sessioninfo_1.2.2 memoise_2.0.1 usethis_2.1.6
#RStudio 2022.02.0+443 "Prairie Trillium" Release (9f7969398b90468440a501cf065295d9050bb776, 2022-02-16) for Windows
#Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) QtWebEngine/5.12.8 Chrome/69.0.3497.128 Safari/537.36
#Obtained with the following code:
#library(lme4)
#library(lmeresampler)
#model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
#nsim <- 100
#set.seed(123)
#B071022ref <- bootstrap(model, .f = fixef, type = "residual", B = nsim, rbootnoise = 0.0001)
model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
nsim <- 100
set.seed(123)
boo <- bootstrap(model, .f = fixef, type = "residual", B = nsim, rbootnoise = 0.0001)
maxreldev <- (B071022ref[["stats"]][,2:5] - boo[["stats"]][,2:5])/B071022ref[["stats"]][,2:5]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
maxreldev <- (B071022ref[["replicates"]] - boo[["replicates"]])/B071022ref[["replicates"]]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
})
test_that("Verify the relatively small effects of rbootnoise = 0.0001 on rep.mean and se. Note, the same seed cannot be followed exactly due to the random noise the rbootnoise feature generates!",{
model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
nsim <- 2000
set.seed(123)
booref <- bootstrap(model, .f = fixef, type = "residual", B = nsim)
set.seed(123) #Note, the set.seed(123) will not be followed as above due to the additional random noise generation!
boo <- bootstrap(model, .f = fixef, type = "residual", B = nsim, rbootnoise = 0.0001)
boodif <- (boo[["stats"]][["rep.mean"]] - booref[["stats"]][["rep.mean"]])/booref[["stats"]][["rep.mean"]]
expect_true(max(abs(boodif)) < 1)
boodif <- (boo[["stats"]][["se"]] - booref[["stats"]][["se"]])/booref[["stats"]][["se"]]
expect_true((max(abs(boodif)) < 1))
})
aloy/lmeresampler documentation built on Dec. 12, 2023, 9:26 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#The purpose of the updated rbootnoise tests is to perform rough comparisons tolerating relatively large deviations from the earlier-acquired reference data. The tests try to catch possible large deviations caused by significant technical issues. Without the highly controlled containers (not applicable in the context of cross-platform R CMD Checks) the exact technical reproducibility cannot be established. For example, it is known that even the different versions of R can cause varying behaviour of the fundamental set.seed() on the same underlying system, an unavoidable technical curiosity accepted by the community. https://stackoverflow.com/questions/47199415/is-set-seed-consistent-over-different-versions-of-r-and-ubuntu
library(lme4, quietly = TRUE)
data(jsp728, package = "lmeresampler")
# ==============================================================================
context("rbootnoise")
# ==============================================================================
test_that("Compare rbootnoise = 0 to lmeresampler 0.2.2 results before the implementation of the feature",{
#Reference data creator: Ilmari Tamminen
load("./reference_data_for_rbootnoise_test_A071022.RData")
#Obtained with the following specs:
#> sessionInfo()
#R version 4.2.1 (2022-06-23 ucrt)
#Platform: x86_64-w64-mingw32/x64 (64-bit)
#Running under: Windows 10 x64 (build 19044)
#Matrix products: default
#locale:
#[1] LC_COLLATE=Finnish_Finland.utf8 LC_CTYPE=Finnish_Finland.utf8 LC_MONETARY=Finnish_Finland.utf8
#[4] LC_NUMERIC=C LC_TIME=Finnish_Finland.utf8
#attached base packages:
#[1] stats graphics grDevices utils datasets methods base
#other attached packages:
#[1] lmeresampler_0.2.2 lme4_1.1-29 Matrix_1.4-1
#loaded via a namespace (and not attached):
#[1] Rcpp_1.0.8.3 lubridate_1.8.0 lattice_0.20-45 tidyr_1.2.1 prettyunits_1.1.1
#[6] ps_1.7.1 rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2 R6_2.5.1
#[11] plyr_1.8.7 HLMdiag_0.5.0 ggplot2_3.3.6 pillar_1.8.1 rlang_1.0.6
#[16] curl_4.3.2 rstudioapi_0.13 minqa_1.2.4 callr_3.7.0 nloptr_2.0.3
#[21] desc_1.4.1 diagonals_6.4.0 devtools_2.4.3 splines_4.2.1 statmod_1.4.37
#[26] stringr_1.4.1 munsell_0.5.0 compiler_4.2.1 janitor_2.1.0 pkgconfig_2.0.3
#[31] pkgbuild_1.3.1 mgcv_1.8-40 tidyselect_1.2.0 tibble_3.1.7 fansi_1.0.3
#[36] crayon_1.5.1 dplyr_1.0.10 withr_2.5.0 MASS_7.3-57 distributional_0.3.1
#[41] ggdist_3.2.0 grid_4.2.1 nlme_3.1-158 gtable_0.3.1 lifecycle_1.0.3
#[46] magrittr_2.0.3 scales_1.2.1 cli_3.3.0 stringi_1.7.8 cachem_1.0.6
#[51] farver_2.1.1 reshape2_1.4.4 fs_1.5.2 remotes_2.4.2 snakecase_0.11.0
#[56] ellipsis_0.3.2 generics_0.1.3 vctrs_0.4.1 boot_1.3-28 tools_4.2.1
#[61] forcats_0.5.2 rcmdcheck_1.4.0 glue_1.6.2 purrr_0.3.4 processx_3.6.1
#[66] pkgload_1.3.0 fastmap_1.1.0 nlmeU_0.70-9 colorspace_2.0-3 xopen_1.0.0
#[71] sessioninfo_1.2.2 memoise_2.0.1 usethis_2.1.6
#RStudio 2022.02.0+443 "Prairie Trillium" Release (9f7969398b90468440a501cf065295d9050bb776, 2022-02-16) for Windows
#Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) QtWebEngine/5.12.8 Chrome/69.0.3497.128 Safari/537.36
#Obtained with the following code:
#library(lme4)
#library(lmeresampler)
#model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
#nsim <- 100
#set.seed(123)
#A071022ref <- bootstrap(model, .f = fixef, type = "residual", B = nsim)
model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
nsim <- 100
set.seed(123)
boo <- bootstrap(model, .f = fixef, type = "residual", B = nsim)
maxreldev <- (A071022ref[["stats"]][,2:5] - boo[["stats"]][,2:5])/A071022ref[["stats"]][,2:5]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
maxreldev <- (A071022ref[["replicates"]] - boo[["replicates"]])/A071022ref[["replicates"]]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
})
test_that("Compare rbootnoise = 0.0001 to the results of the first implementation of the feature",{
#Reference data creator: Ilmari Tamminen
load("./reference_data_for_rbootnoise_test_B071022.RData")
#Obtained with the following specs:
#> sessionInfo()
#R version 4.2.1 (2022-06-23 ucrt)
#Platform: x86_64-w64-mingw32/x64 (64-bit)
#Running under: Windows 10 x64 (build 19044)
#Matrix products: default
#locale:
#[1] LC_COLLATE=Finnish_Finland.utf8 LC_CTYPE=Finnish_Finland.utf8 LC_MONETARY=Finnish_Finland.utf8
#[4] LC_NUMERIC=C LC_TIME=Finnish_Finland.utf8
#attached base packages:
#[1] stats graphics grDevices utils datasets methods base
#other attached packages:
#[1] lmeresampler_0.2.1.99999 lme4_1.1-29 Matrix_1.4-1
#loaded via a namespace (and not attached):
#[1] Rcpp_1.0.8.3 lubridate_1.8.0 lattice_0.20-45 tidyr_1.2.1 prettyunits_1.1.1
#[6] ps_1.7.1 rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2 R6_2.5.1
#[11] plyr_1.8.7 HLMdiag_0.5.0 ggplot2_3.3.6 pillar_1.8.1 rlang_1.0.6
#[16] curl_4.3.2 rstudioapi_0.13 minqa_1.2.4 callr_3.7.0 nloptr_2.0.3
#[21] desc_1.4.1 devtools_2.4.3 diagonals_6.4.0 splines_4.2.1 statmod_1.4.37
#[26] stringr_1.4.1 munsell_0.5.0 compiler_4.2.1 janitor_2.1.0 pkgconfig_2.0.3
#[31] pkgbuild_1.3.1 mgcv_1.8-40 tidyselect_1.1.2 tibble_3.1.7 fansi_1.0.3
#[36] crayon_1.5.1 dplyr_1.0.10 withr_2.5.0 MASS_7.3-57 distributional_0.3.1
#[41] ggdist_3.2.0 grid_4.2.1 nlme_3.1-158 gtable_0.3.1 lifecycle_1.0.3
#[46] magrittr_2.0.3 scales_1.2.1 cli_3.3.0 stringi_1.7.8 cachem_1.0.6
#[51] farver_2.1.1 reshape2_1.4.4 fs_1.5.2 remotes_2.4.2 snakecase_0.11.0
#[56] ellipsis_0.3.2 generics_0.1.3 vctrs_0.4.1 boot_1.3-28 tools_4.2.1
#[61] forcats_0.5.2 rcmdcheck_1.4.0 glue_1.6.2 purrr_0.3.4 processx_3.6.1
#[66] pkgload_1.3.0 fastmap_1.1.0 nlmeU_0.70-9 colorspace_2.0-3 xopen_1.0.0
#[71] sessioninfo_1.2.2 memoise_2.0.1 usethis_2.1.6
#RStudio 2022.02.0+443 "Prairie Trillium" Release (9f7969398b90468440a501cf065295d9050bb776, 2022-02-16) for Windows
#Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) QtWebEngine/5.12.8 Chrome/69.0.3497.128 Safari/537.36
#Obtained with the following code:
#library(lme4)
#library(lmeresampler)
#model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
#nsim <- 100
#set.seed(123)
#B071022ref <- bootstrap(model, .f = fixef, type = "residual", B = nsim, rbootnoise = 0.0001)
model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
nsim <- 100
set.seed(123)
boo <- bootstrap(model, .f = fixef, type = "residual", B = nsim, rbootnoise = 0.0001)
maxreldev <- (B071022ref[["stats"]][,2:5] - boo[["stats"]][,2:5])/B071022ref[["stats"]][,2:5]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
maxreldev <- (B071022ref[["replicates"]] - boo[["replicates"]])/B071022ref[["replicates"]]
maxreldev <- max(abs(maxreldev))
comparison <- (maxreldev < 1)
expect_true(comparison, info = NULL, label = NULL)
})
test_that("Verify the relatively small effects of rbootnoise = 0.0001 on rep.mean and se. Note, the same seed cannot be followed exactly due to the random noise the rbootnoise feature generates!",{
model <- lmer(mathAge11 ~ mathAge8 + gender + class + (1 | school), data = jsp728)
nsim <- 2000
set.seed(123)
booref <- bootstrap(model, .f = fixef, type = "residual", B = nsim)
set.seed(123) #Note, the set.seed(123) will not be followed as above due to the additional random noise generation!
boo <- bootstrap(model, .f = fixef, type = "residual", B = nsim, rbootnoise = 0.0001)
boodif <- (boo[["stats"]][["rep.mean"]] - booref[["stats"]][["rep.mean"]])/booref[["stats"]][["rep.mean"]]
expect_true(max(abs(boodif)) < 1)
boodif <- (boo[["stats"]][["se"]] - booref[["stats"]][["se"]])/booref[["stats"]][["se"]]
expect_true((max(abs(boodif)) < 1))
})
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